SLIDER: A Generic Metaheuristic for the Discovery of Correlated Motifs in Protein-Protein Interaction Networks
Issue No. 05 - September/October (2011 vol. 8)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.17
Peter Boyen , Hasselt University, Diepenbeek and Transnational University of Limburg
Dries Van Dyck , Hasselt University, Diepenbeek and Transnational University of Limburg
Frank Neven , Hasselt University, Diepenbeek and Transnational University of Limburg
Roeland C.H.J. van Ham , Applied Bioinformatics - Plant Research International, Wageningen
Aalt D.J. van Dijk , Applied Bioinformatics - Plant Research International, Wageningen
Correlated motif mining (cmm) is the problem of finding overrepresented pairs of patterns, called motifs, in sequences of interacting proteins. Algorithmic solutions for cmm thereby provide a computational method for predicting binding sites for protein interaction. In this paper, we adopt a motif-driven approach where the support of candidate motif pairs is evaluated in the network. We experimentally establish the superiority of the Chi-square-based support measure over other support measures. Furthermore, we obtain that cmm is an np-hard problem for a large class of support measures (including Chi-square) and reformulate the search for correlated motifs as a combinatorial optimization problem. We then present the generic metaheuristic slider which uses steepest ascent with a neighborhood function based on sliding motifs and employs the Chi-square-based support measure. We show that slider outperforms existing motif-driven cmm methods and scales to large protein-protein interaction networks. The slider-implementation and the data used in the experiments are available on http://bioinformatics.uhasselt.be.
Graphs and networks, biology and genetics.
Peter Boyen, Dries Van Dyck, Frank Neven, Roeland C.H.J. van Ham, Aalt D.J. van Dijk, "SLIDER: A Generic Metaheuristic for the Discovery of Correlated Motifs in Protein-Protein Interaction Networks", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. , pp. 1344-1357, September/October 2011, doi:10.1109/TCBB.2011.17